





import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase 					=      logging.get_logger(__name__)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Dict     ):
		SCREAMING_SNAKE_CASE__								=   WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase__       , config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""projector.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""projector.bias"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.post_net.linear.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.post_net.linear.bias"""]
		return model






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Dict       , UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase__       , config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.linear.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.linear.bias"""]
		return model






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   WavaVecaForXVector.from_pretrained(UpperCamelCase__       , config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""connector.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""connector.bias"""]
		for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
				SCREAMING_SNAKE_CASE__								=   downstream_dict[
				    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
				]
				SCREAMING_SNAKE_CASE__								=   downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""objective.W"""]
		return model






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]     ):
		SCREAMING_SNAKE_CASE__								=   torch.load(UpperCamelCase__       , map_location="""cpu"""     )

		SCREAMING_SNAKE_CASE__								=   checkpoint["""Downstream"""]

		SCREAMING_SNAKE_CASE__								=   WavaVecaConfig.from_pretrained(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   WavaVecaFeatureExtractor.from_pretrained(
		    UpperCamelCase__       , return_attention_mask=UpperCamelCase__       , do_normalize=UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   hf_config.architectures[0]
		if arch.endswith("""ForSequenceClassification"""     ):
				SCREAMING_SNAKE_CASE__								=   convert_classification(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
		elif arch.endswith("""ForAudioFrameClassification"""     ):
				SCREAMING_SNAKE_CASE__								=   convert_diarization(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
		elif arch.endswith("""ForXVector"""     ):
				SCREAMING_SNAKE_CASE__								=   convert_xvector(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
		else:
				raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''     )

		if hf_config.use_weighted_layer_sum:
				SCREAMING_SNAKE_CASE__								=   checkpoint["""Featurizer"""]["""weights"""]

		hf_feature_extractor.save_pretrained(UpperCamelCase__     )
		hf_model.save_pretrained(UpperCamelCase__     )


if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser()
	parser.add_argument(
	    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
	)
	parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
	parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
	parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
	_lowerCamelCase 					=      parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GraphormerForGraphClassification""",
					    """GraphormerModel""",
					    """GraphormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_graphormer import (
										    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GraphormerForGraphClassification,
										    GraphormerModel,
										    GraphormerPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



